Abstract : The near-term goal of the project was to create models of cortical neurons and to implement these on a relatively fast platform. This was done. These models have been used to create insights into the computational differences between neurons and the processing elements typically used in connectionistic studies. The longer-term goal was to abstract these computations into a more efficient form, implement them into learning circuits, and ultimately figure out how to imbed these into low-power, reliable circuit-level VLSI. We have succeeded in the abstractions, and have begun to implement these into circuits that learn and encode time. We are exploring VLSI options.